Abstract
Human operators of remote and semi-autonomous systems must have a high level of executive function to safely and efficiently conduct operations. These operators face unique cognitive challenges when monitoring and controlling robotic machines, such as vehicles, drones, and construction equipment. The development of safe and experienced human operators of remote machines requires structured training and credentialing programs. This review critically evaluates the potential for incorporating neurotechnology into remote systems operator training and work to enhance human-machine interactions, performance, and safety. Recent evidence demonstrating that different noninvasive neuromodulation and neurofeedback methods can improve critical executive functions such as attention, learning, memory, and cognitive control is reviewed. We further describe how these approaches can be used to improve training outcomes, as well as teleoperator vigilance and decision-making. We also describe how neuromodulation can help remote operators during complex or high-risk tasks by mitigating impulsive decision-making and cognitive errors. While our review advocates for incorporating neurotechnology into remote operator training programs, continued research is required to evaluate the how these approaches will impact industrial safety and workforce readiness.
Introduction
The growth and adoption of robotics, artificial intelligence (AI), and industrial automation is changing the nature of human-machine interactions (HMI). Industrial sectors like shipping, excavation, road making, construction, and commercial truck driving are becoming increasingly reliant on remotely operated and semi-autonomous systems, which requires training a new generation of skilled operators. These remote and semi-autonomous systems require trained personnel, who are capable of monitoring complex sensor information, interpreting real-time data, and making rapid decisions in dynamic environments. The use of small unmanned aerial systems (sUAS) or unmanned aerial vehicles (UAVs) is changing the workforce across many industries (Tezza and Andujar, 2019; ; Rao et al., 2016; Merkert and Bushell, 2020) (Figure 1). Similarly, the past decade has seen immense growth in the use of semi-autonomous mining equipment, remotely operated agricultural machines, and robotic cargo handling systems. Collectively, these remotely operated machines are revolutionizing industrial efficiency, productivity, and safety (Tezza and Andujar, 2019; Schönböck et al., 2022; Wanasinghe et al., 2021; Pizoń and Gola, 2023; Soori et al., 2023). These modern systems introduce new cognitive challenges for HMI’s and our workforce. In the present review, we evaluate literature suggesting these challenges can be addressed by incorporating neurotechnology into advanced training and simulation methods.
FIGURE 1
Remote operation of drones and robotic machinery can be a cognitively demanding task that requires distributed attention, efficient decision making, and physical multi-tasking under a high psychological stress load (; Ljungblad et al., 2021; Lee et al., 2022). Remote operators face significant cognitive and sensorimotor challenges that differ from traditional vehicle operation. They must continuously process and interpret multi-modal sensory data from cameras, ultrasonic and laser range sensors, and other real-time data transmission. Due to various hardware, firmware, software, and human interface variables, the streaming and display of data can be wrought with timing delays or inconsistencies challenging the operator. For example, the reliance on indirect visual input rather than direct line-of-sight decreases situational awareness, which requires operators to develop strong spatial reasoning and cognitive control skills. Other performance burdens arise due to the complexity and timing differences in multi-sensory integration and processing demands placed upon teleoperators. For example, human processing of digitized video streams or visual feeds may not align with their endogenous proprioceptive, cognitive, or real-world visual processing cues. Furthermore, fine motor control through joysticks or haptic interfaces requires sustained attention due to the precise coordination between cognitive decision making and sensorimotor execution (Figure 2). This often leads to operator mental and physical fatigue over prolonged operations. Operators must also contend with unexpected environmental changes, system failures, and the psychological strain of high-stakes decision-making. Thus, cognitive endurance and robust executive function are essential for the optimal performance of remote operators.
FIGURE 2
The robotics industry is undergoing an evolution in which artificial systems are beginning to adapt to and learn human operator variables. To advance control and command systems capable of cognitive co-evolution, neurotechnology can deliver methods and devices centered on helping the human operator learn and adapt to these robotic and semi-autonomous systems. Developing solutions intended to enhance remote operator training and performance represents an immense opportunity for incorporating neurotechnology to improve learning, cognition, and stress responses for human operators of semi-autonomous robotic machines and systems. Training systems and programs have recently evolved to include high-fidelity simulators that provide realistic operational environments, which reflect the operational experiences and complexities of real-world tasks (Figure 2). Training of remote operators using realistic simulators improves safety and reduce costs across many industries where humans monitor and control the work of semi-autonomous machines (Schönböck et al., 2022; Lee et al., 2022; ; ; Han et al., 2023; Shringi et al., 2022; Warren et al., 2023; Xu and Zheng, 2021). Government and industry regulatory bodies including the Occupational Health and Safety Administration (OSHA), Department of Transportation (DOT), Federal Aviation Administration (FAA), US Department of Agriculture (USDA), and many others have engaged in the development of innovative training approaches to ensure workforce readiness as industries increasingly rely on HMI’s and remotely operated machines (; ; Goode et al., 2013; Veitch and Andreas Alsos, 2022).
Like the growth of the robotics industry, the field of neuroengineering has also experienced recent growth. There have been many advances in the development and commercialization of methods and devices for sensing and modulating human brain activity, behavior, and cognition. Neuromodulation techniques like transcranial electrical stimulation (tES), transcranial focused ultrasound (tFUS) neuromodulation and transcutaneous vagus nerve stimulation (tVNS) offer promising solutions to optimize training and performance outcomes for remote operators. As described below, these methods work by enhancing neuroplasticity, which helps operators learn better and more easily adapt to complex control systems. Recent studies demonstrate that tES and tVNS can improve resilience, decision-making, multimodal attention, vigilance, and cognitive flexibility (; McIntire et al., 2023; ; Hemmerich et al., 2024). By integrating these noninvasive brain stimulation methods into training programs, organizations can accelerate skill acquisition and optimize cognitive function, leading to a more capable and adaptable workforce. Furthermore, the incorporation of neurofeedback and AI-driven sensor enhancements into HMI training can improve situational awareness and real-time cognitive performance. Using electroencephalography (EEG) or functional near-infrared spectroscopy (fNIRS), neurofeedback approaches can provide operators with real-time monitoring of cognitive states while helping them regulate attention and optimize decision-making (; Kim et al., 2014; ; Khan and Hong, 2017; ). With drones and other autonomous machines becoming an integral part of everyday industrial and commercial operations, investment in neurotechnology-driven operator training and human-machine optimization will be crucial in shaping the future of autonomous systems management.
Synthetic environments and realistic training programs for enhancing remote operator performance
The increasing reliance on remote, robotic, and semi-autonomous systems across industries requires the development of structured training programs to ensure operators acquire the necessary cognitive and technical skills to perform safely and efficiently. New technologies enable the delivery of realistic training programs in synthetic environments designed to enhance a remote operator’s sensorimotor skills, situational awareness, executive function, and decision-making while cooperatively working with robotic machinery (Figure 2B). Unambiguously, a wealth of literature shows realistic training simulations enhance workforce preparedness across multiple industries, including UAV operations, excavation, forestry, mining, and robotic-assisted surgery (Schönböck et al., 2022; ; Shringi et al., 2022; ; Liu et al., 2013; Sridhar et al., 2017). By utilizing simulation-based training and virtual reality (VR) environments, these programs provide hands-on experience in a controlled setting, allowing trainees to develop critical competencies without exposing them to real-world hazards.
High-fidelity simulators recreate real-world operational scenarios with precise control interfaces and environmental conditions, allowing operators to practice responding to emergency situations, system failures, and high-pressure decision-making tasks (Figure 2). For example, UAV operator performance is enhanced following training in simulated flight environments to develop precision in navigating airspace, reacting to unexpected obstacles, and coordinating with autonomous flight systems (Schmidt et al., 2022; Sakib et al., 2021; Somerville et al., 2024). In mining, forestry, and excavation, for example, operators trained through realistic VR-based simulation programs demonstrate greater proficiency in navigating hazardous terrain, managing automated excavation equipment, and optimizing resource extraction processes compared to those trained using traditional methods. Similarly, robotic surgeons undergo rigorous simulator-based training before transitioning to live procedures, ensuring proficiency in remote-controlled surgical techniques before working on actual patients (Sridhar et al., 2017; Moit et al., 2019; Mallela et al., 2022). One of the primary advantages of simulation-based training is its ability to improve operator safety and efficiency. By exposing trainees to high-risk scenarios in a risk-free environment, they are better equipped to handle real-world stressors when performing complex remote operations.
Adaptive factors approaches to enhancing human-machine interactions
The effectiveness of realistic training programs can be further enhanced using adaptive technologies designed to enhance human-in-the-loop performance. Robotic and semi-autonomous machines do not experience physiological stress and are less prone to cognitive fatigue and failure than human operators. For example, by optimizing the cognitive workload of trainees during prolonged training implementing adaptive training logic and methods have been shown to enhance virtual F-16 cockpit training compared to non-adaptive VR methods (). The use of haptics to provide tactile sensory input and somatosensory feedback related to texture, hydraulics, or force during virtual training has been shown to improve robotic teleoperator training outcomes and performance compared to control approaches not using haptics (; Patel et al., 2022; Gani et al., 2022; Zhu et al., 2021; Yang et al., 2021; Xia et al., 2023). A recent study compared the influence of multi-modal sensory inputs on teleoperator performance. It was shown the use of stereoscopic 3D or VR displays improve task performance and accuracy by 40% compared to monocular displays and integration with vibrotactile and auditory feedback further improve performance (Triantafyllidis et al., 2020). Deeper investigations have studied the multisensory impact of encoding force with light, sound, and vibration on cognitive workload using EEG during robotic machine training (Haruna et al., 2021). Haruna and colleagues (2021) found that strategically positioned visual feedback encoding robotic force produced the most efficient haptic approach for reducing cognitive load or mental work during training (Haruna et al., 2021). Improving our physiological models of stress and mental workload can greatly enhance remote operator training, performance, and safety (Sakib et al., 2021). As further discussed below, neuroscience-guided approaches can further enable the reduction of cognitive fatigue by modulating arousal while distributing cognitive resources across different sensory (somatosensory, visual, and auditory cortex) and executive decision-making regions of the brain (prefrontal, temporoparietal, and cingulate cortex).
The use of haptic methods to improve teleoperator training and performance presents several interesting oppurtunities for advance the design and human factors elements of HMI’s. The somewhat recent development of soft, wearable sensors and actuators have led to the development of wearable haptic interfaces for improving the sensory and situational awareness of remote operators (Yin et al., 2021; Grasso et al., 2023; ). More specifically, several wearable embodiments like haptic sleeves, gloves, and shoes have been shown to enable neuromorphic control, enhanced environmental awareness, and overall improved teleoperator performance (Macchini et al., 2020; Thakur et al., 2024; ). Efforts to engineer innovative haptic interfaces will make HMI’s more usable and reliable for integration into advanced training programs. For example, using soft, compliant electroactive hydrogels or polymers for sensing and actuation can improve the interface between human operators and hardware control systems (Xu et al., 2015). Stretchable hydraulic amplified actuators can be 3D printed and personalized to a user’s fingertips to enhancing cutaneous haptics without affecting dexterity (Grasso et al., 2023). State-of-the-art bioelectronic methods recently led to the engineering artificial ‘e-skin’ that can be used for neuromorphic robotic control and sensorimotor loop integration in HMI’s (Wang et al., 2023). Given observations that visual haptics can enhance operator performance while reducing cognitive load, the use of electroluminescent force sensors in tactile skin may represents an interesting approach for enhancing teleoperator HMI’s (). The literature suggests that combining advanced bioelectronic interfaces with proven realistic training methods can significantly enhance the skill training, cognition, safety, and preparedness of drone, robotic, and remote machine operators.
Noninvasive neuromodulation for enhancing human-machine cognition and teleoperator performance
Noninvasive neuromodulation approaches, such as transcranial magnetic stimulation (TMS), transcranial electrical stimulation (tES), transcranial focused ultrasound (tFUS), and transcutaneous vagus nerve stimulation (tVNS) have gained attention the past couple decades for their ability to treat various neurologic and psychiatric disorders (Tyler et al., 2017; ). These multimodal neuromodulation methods have also gained attention in consumer electronics, enterprise businesses, sports, and health industries for their ability to improve brain plasticity, enhance cognition, reduce stress, improve sleep, and optimize decision-making (Herrmann et al., 2013; ; Grover et al., 2023; ; Tyler, 2017; ). The noninvasive neuromodulation literature provides compelling evidence that these methods are safe for use in healthy humans suggesting they may be readily integrated into remote operator training programs (Tyler, 2017; Santarnecchi et al., 2015; Rossi et al., 2021; Kim et al., 2022; ). We are motivated to specifically evaluate wearable approaches with real-time modulation capabilities that can be easily incorporated into workforce training and performance programs. Thus, we restrict our further discussion of neuromodulation below to tES, tVNS, and tFUS methods. Presently, the electrical power and magnetic resonance image-guided neuronavigation recommendations for TMS makes it difficult to deploy in scalable training and performance enhancement solutions across industries.
Both tES and tVNS methods involve the transmission of weak (<4 mA) electrical currents across the skin. The general tES method embodies different approaches including transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) (Paulus, 2011). These methods different electrode sizes, shapes, densities, and polarities to deliver a DC or AC currents (1–20,000 Hz) across the skin of the scalp to bias the activity and plasticity of targeted cortical regions (Figure 3A). Another tES approach known as transcranial random noise stimulation (tRNS) is just like tACS, but delivers different pulsed or AC stimuli at random or pseudo-random frequencies (Paulus, 2011; ). Using transcutaneous electrical nerve stimulation approaches, tVNS may be achieved by targeting cervical branches of the vagus on the side of the neck in a method known as transcutaneous cervical vagus nerve stimulation (tcVNS; Figure 3B). Alternatively, transcutaneous auricular vagus nerve stimulation (taVNS) can be achieved by targeting auricular branches of the vagus nerve using pulsed stimuli located on the external ears (Figure 3C). All these methods have well understood mechanisms of action, offer comfortable and wearable human factors designs, can be safely used, and have been shown to improve various aspects of human behavior and cognition that can benefit remote operator performance as discussed below.
FIGURE 3
Transcranial electrical stimulation
The modern resurgence of tES was spawned by observations demonstrating that tDCS produces neurophysiological changes in cortical network excitability and brain plasticity (Nitsche and Paulus, 2000). Following this seminal observation, the field of noninvasive neuromodulation experienced explosive growth. A series of pioneering studies conducted by the US Air Force Research Laboratories provided the first evidence that cortical tES can be effective for enhancing remote operator cognition, training, performance (McKinley et al., 2011; McKinley et al., 2013; McIntire et al., 2014; Nelson et al., 2014). McKinley and colleagues (2013) initially demonstrated that 30-min tDCS (2 mA) can enhance remote operator training by producing a 25% improvement on visual search tasks compared to controls (McKinley et al., 2013). This work laid the foundation for follow-up investigations demonstrating tDCS applied to prefrontal cortex improves attention, reduces mental fatigue, and enhances multi-tasking during sustained, complex tasks that are ecologically valid and contextually relevant to remote drone and machine operations (McIntire et al., 2014; Nelson et al., 2014; Nelson et al., 2016).
Using EEG and fNIRS to record brain activity, it has been shown that tDCS delivered to the primary motor cortex (M1) increases neuronal excitability and enhances sensorimotor skill learning outcomes on a flight simulator (). Targeting M1 using tDCS has been shown to enhance learning and performance on several other skilled motor tasks, such as sequential tapping and controlled force pinching tasks (Saucedo Marquez et al., 2013; Orban de Xivry and Shadmehr, 2014). Studies evaluating the influence of tES on motor vehicle operator performance have also shown improvements to executive function and cognitive performance. For example, a recent study demonstrated that 30-min tRNS delivered during VR truck driving simulation tasks significantly reduced mental fatigue compared to controls (). Another recent study showed tRNS can accelerate learning in VR environments (Neri et al., 2025). These data suggest that hybrid neurostimulation strategies incorporating VR or realistic simulations with tES may be particularly beneficial for remote operator training. Given that tDCS has been shown to protect against diminished executive vigilance decrement under high cognitive loads (Hemmerich et al., 2024), its application in long duration teleoperation training or missions may also significantly reduce operator fatigue and improve safety. Other research on driving simulators indicates that attentional control and reaction times can be significantly improved using tDCS, which further indicates tES methods holds promise for optimizing operator performance in dynamic, virtual, training or work environments (). Further research is required to identify the optimal parameters and brain targets for using tES methods before and during training or work procedures to improve remote operator attention and learning.
Evidence from investigations into the use of tDCS for treating various clinical conditions has evolved into insights that hold promise for enhancing general cognitive performance. Several studies have shown tDCS delivered to the prefrontal cortex can improve executive functions like working memory, impulse control, and cognitive flexibility, as well as time perception in children and adults with attention-deficit hyperactivity disorder (ADHD) (Nejati et al., 2020; Nejati et al., 2021; Nejati et al., 2024; Leffa et al., 2022). These observations combined with those described above showing tDCS improves driver skill training and performance, suggest tES may be useful for improving the safety of drivers with attention disorders like ADHD. In patient populations who exhibit problems with impulse control, such as those with gambling disorders, prefrontal tDCS has been shown to enhance cognitive control and decision making by reducing risk taking while improving cognitive flexibility (Soyata et al., 2019; Gilmore et al., 2018). In healthy adults tDCS can improve working memory, decision making, and impulse control (Ke et al., 2019; Ruf et al., 2017; Ouellet et al., 2015; Yang et al., 2017). Likewise, tACS delivered at theta and gamma frequencies can improve working memory and recall in healthy adults (Jaušovec et al., 2014; Hoy et al., 2015). Further, tACS delivered 4 days in a row can produce significant improvements in working and long-term memory that last up to 1 month in healthy aged adults (Grover et al., 2022). These cognitive enhancements would be beneficial to remote operators to improve skill learning and retention. The increasing prevalence of teleoperated machines and construction equipment justifies deeper explorations into how neuromodulation can improve attention and support adaptive decision-making by reducing risk taking, particularly in hazardous and high-risk environments (Lee et al., 2022). While the literature clearly supports these approaches, further investigations are required to understand how different stimulation intensities, frequencies, and locations differentially affect executive function during realistic remote operator training scenarios.
Transcutaneous vagus nerve stimulation
Using pulsed electrical currents to modulate vagus nerve fibers located in different locations along the side of the neck or external ear has gained attention for its safe ability to modulate autonomic nervous system activity, inflammation, neuroplasticity, attention, stress, learning, mood, and sleep (Tyler, 2017; Kim et al., 2022; Liu et al., 2020; Wang et al., 2021; ; Verma et al., 2021; Sant'Anna et al., 1992; Tan et al., 2023; Urbin et al., 2021; ; Srinivasan et al., 2023; Ma et al., 2022; Phillips et al., 2021; Prescott and Liberles, 2022; Henry, 2002; Thayer and Sternberg, 2006). It is well established that noninvasive VNS works by modulating the activity of the locus coeruleus and ascending reticular activating system located in brainstem. This primary action alters the release of norepinephrine across large brain regions and organs in the body produces changes in activity, arousal, and plasticity (Urbin et al., 2021; Frangos et al., 2015; Sharon et al., 2021; Schuerman et al., 2021; Frangos and Komisaruk, 2017). Other neurotransmitters like acetylcholine (Martin et al., 2024; Pavlov and Tracey, 2005) and serotonin (Hulsey et al., 2019; Manta et al., 2009) have also been shown to regulate immune function and brain plasticity following transcutaneous VNS. Targeting cervical vagus nerve fibers using tcVNS is FDA cleared to treat headache, while targeting auricular vagus nerve fibers using taVNS is FDA cleared to treat opiate withdrawal. When proper precautions and methods are used, tcVNS and taVNS pose a low-risk or non-significant risk and have numerous other health and wellness applications. In fact, several tcVNS and taVNS devices are distributed over the counter as General Wellness devices when not used to treat or diagnose a disease or medical condition. Many studies in healthy volunteers demonstrate these noninvasive neuromodulation approaches can enhance human cognition and performance as further discussed below.
Several studies have shown that both tcVNS and taVNS can reduce the sympathetic nervous system activity, as well as the psychological and neurophysiological symptoms of stress (Trifilio et al., 2023; ; ; Machetanz et al., 2021a; Machetanz et al., 2021b; Gurel et al., 2020; Moazzami et al., 2023). A recent randomized, sham-controlled study demonstrated that taVNS produced significant changes in bottom-up neurophysiological arousal leading to significantly improved impulse control during emotional tasks. It also been demonstrated that taVNS can improve cognitive control during multi-tasking to enhance performance (Sommer et al., 2023). The ability of taVNS to dampen stress responses is likely a contributing factor to the improved performance observed under high cognitive and emotional loads. In fact, taVNS has been shown to improve action control performance and response selection when task demands are high (Jongkees et al., 2018). The reduction of stress by taVNS also suggest it can be used following virtual or realistic training sessions to improve rest and recovery from mental strain or fatigue (). Several studies have also shown that both taVNS and tcVNS can improve human learning and memory (Phillips et al., 2021; Kaan et al., 2021; Pandža et al., 2020; Jacobs et al., 2015; Olsen et al., 2023; Zhang et al., 2020; ; Ridgewell et al., 2021; Miyatsu et al., 2024; ; Klaming et al., 2022).
In addition to direct effects on neuroplasticity, the influence of tVNS on learning and memory can be also attributed to its ability to modulate human cortical arousal and attention (Sharon et al., 2021; Trifilio et al., 2023; Miyatsu et al., 2024; ; Rufener et al., 2018; Giraudier et al., 2024; Tyler et al., 2019). Recent studies show that taVNS can significantly improve motor action planning, enhance motor sequence learning, and improve associated motor cortex efficiency (; ). It has also been demonstrated that taVNS can improve human working memory (Sun et al., 2021) and cognitive flexibility (). When subjects are sleep deprived, taVNS has been shown to reduce fatigue and improve working memory (Zhao et al., 2023), while tcVNS also reduces fatigue and improves multitasking performance (McIntire et al., 2021). Attention, working memory, and cognitive control networks are critical in decision making (; ; McGuire and Botvinick, 2010; ). Impaired working memory has been shown to underlie impulsivity in decision making (Hinson et al., 2003). These different approaches to enhancing cognition can prove beneficial to improving human-machine operator training and performance (Figure 4).
FIGURE 4
Impulsive decision-making by vehicle and remote operators represents a major risk since it can lead to dangerous situations or crashes injuring people and property. Interestingly, studies have demonstrated that taVNS can produce more efficient neural processing requiring fewer resources to achieve cognitive control (Pihlaja et al., 2020), as well as to improve cognitive control or adaptation in response to conflict (). As cited above, taVNS improves action control performance when task demands are high (Jongkees et al., 2018) and enhances cognitive control during multi-tasking (Sommer et al., 2023). These data indicate tVNS may improve cognitive control and flexibility, enabling vehicle and remote machine operators to switch between tasks and manage multiple streams of information more efficiently (Figure 4). Given that cognitive training has been shown to reduce motor vehicle collision involvement by up to 50% in older drivers (), similar cognitive training approaches combined with tVNS to reinforce learning outcomes and executive control may help mitigate human error in remote industrial robotics and autonomous vehicle supervision. It has also been shown that taVNS boosts human drive to work for rewards suggesting it may be useful for improving the motivation of tele-operators, drivers, and remote pilots to engage in reward-based training (Neuser et al., 2020).
Studies have shown that tcVNS and taVNS provide roughly equal benefits. The choice to use one method or approach over another can be distilled down to human factors issues. One may consider whether they need hands-free capabilities for real-time modulation. One may also consider how one makes the operator feel from a sensory stimulation standpoint as this is becoming one of the key issues related to the use of tVNS for cognitive enhancement. For enhancing cognition and reducing stress it is critical that the user or patient has a comfortable experience where the stimulation is just noticeable or not noticeable from a sensory stimulation perspective. Otherwise the off target sensory effects that emerge as distracting and uncomfortable sensations from electrical stimulation can override intended tVNS outcomes (Miyatsu et al., 2024; Jigo et al., 2024). In other words, modulating vagus nerve activity using transcutaneous, pulsed electrical nerve stimulation methods can both activate and suppress sympathetic activity (stress) depending on many variables including the electrode interface, user sensation and comfort, stimulus frequency, pulse duration, ease of use, and others (Figures 3B,C). This has been observed in studies evaluating the influence of cognitive load and tVNS on pupillometry as a noradrenergic-related measure of neurophysiological arousal (; Urbin et al., 2021; Phillips et al., 2021; Sharon et al., 2021; Pandža et al., 2020; Tyler et al., 2019; Ramakrishnan et al., 2021). Efforts aimed at improving human factors or neuroergonomics of tVNS can be combined with work to advance neurostimulation algorithms and parameters for continuing to enhance the electrical sensation experiences, ease of use, user comfort, and efficacy. This approach should prove valuable considering provocative demonstrations that high frequency (20,000 Hz), sub-perceptual taVNS produces significant changes in cerebellar activity (), as well as changes in the functional connectivity of the prefrontal cortex, cingulate cortex, and insula.
Using vibrotactile and haptic stimulation of the external ear and vagus nerve has also shown potential for enhancing human-computer interactions. It has been argued the tactile sensitivity of the external ear has been overshadowed by its auditory functions and that haptic stimulation of the ear represents an opportunity for information transfer (Lee et al., 2019). This point reiterates the importance of ensuring sensations from stimulation are not distracting to the user (Tyler, 2025). Lee and colleagues (2019) demonstrated small ear worn haptic stimulation devices could encode environmentally relevant spatiotemporal information by stimulating six different locations on the external ear. In an adaptive embodiment, ear haptics were demonstrated as a human-computer interface to enhance the experience of virtual reality applications for deaf and hard-of-hearing (DHH) individuals (Mirzaei et al., 2020). Haptic stimulation of the ear can convey sound direction in relation to DHH users during a VR experience when a system was not universally designed and intended for hearing enabled persons using spatially encoded audio to simulate sound distance (Mirzaei et al., 2020). Combining different methods of vibrotactile and electrical stimulation may open new possibilities for modulation of human-machine cognition, such as to improve situational awareness and multimodal attention. Future human factors studies and engineering efforts should aim to identify and translate these methods to interoperate with traditional methods, such as cognitive training that have proven indispensable.
Transcranial focused ultrasound neuromodulation
Neuromodulation by transcranial focused ultrasound (tFUS) provides unrivaled spatial resolution and precision compared to other noninvasive modalities (Tyler et al., 2018). This method enables noninvasive, deep-brain stimulation in humans across many brain regions. Advancing tFUS or transcranial ultrasound stimulation (TUS) for human-machine interfaces has been a major interest since our pioneering studies demonstrating that low-intensity pulsed ultrasound can stimulate intact brain circuits (Tyler et al., 2008; Tufail et al., 2010). Ultrasonic neuromodulation and tFUS work when the sound waves of low-intensity, pulsed ultrasound mechanically modulate the electrical activity of brain circuits and nerves by altering the activity of pressure-sensitive ion channels, transporters, and neuronal membranes (Tyler et al., 2018; Tyler et al., 2008; Naor et al., 2016; ). Depending on the frequency of ultrasound implemented, the spatial resolution of focused ultrasound for neuromodulation can achieve single cell resolutions in vitro, to a few microns in nerves, to a couple millimeters when delivered transcranial to deep-brain regions (Tyler et al., 2018; Naor et al., 2016). As discussed below, several lines of evidence demonstrate that tFUS can be useful for modulating human-machine cognition and interactions (Tyler, 2017; Tyler et al., 2018; Lee et al., 2024; Kim T. et al., 2021).
Modulation of human cortex to influence sensory processing and motor performance during training or teleoperation of machines may enhance human operator performance. The first functional evidence that 0.5 MHz tFUS can noninvasively modulate human brain activity recorded by EEG demonstrated that tactile discrimination abilities are enhanced following brief delivery of tFUS to the hand region of primary somatosensory cortex (Legon et al., 2014). In similar functional studies, tFUS delivered to the hand region of primary motor cortex has been shown to modulate brain circuit activity and enhance reaction times (Legon et al., 2018a; Fomenko et al., 2020). These approaches are particularly interesting options to optimize human interactions with joysticks, buttons, or hand controls by enhancing sensorimotor performance. A recent breakthrough study demonstrated the accurate, reliable, and individualized, deep-brain targeting of tFUS to the globus pallidus internus of the basal ganglia in Parkinson’s patients (). These observations combined with others demonstrating deep-brain modulation of motor and sensory thalamic nuclei (Legon et al., 2018b; ; ) open the possibility of using tFUS to influence different nodes in human sensorimotor circuits for enhancing teleoperator performance. Another potential avenue is to modulate visual spatial processing by TUS. In support of this approach a recent study in healthy human volunteers showed tFUS delivered to visual cortex area 5 (V5) enhanced feature-based attention to motion by modulating activity in the dorsal visual processing pathway (Kosnoff et al., 2024). Interestingly, Kosnoff and colleagues (2024) also found this performance improvement led to reduced errors when subjects performed a spelling task using an EEG-based brain-computer interface (BCI) (Kosnoff et al., 2024). These findings raise the possibility of using tFUS to modulate human visual cortex and attention during robotic and semi-autonomous operation, as well as to improve BCI performance.
Other targets and approaches for modulating the cognitive function of remote operators or pilots with tFUS should be considered. A recent study demonstrated that tFUS improved cognitive control when delivered to the right inferior frontal gyrus (rIFG), which helps to regulate cognitive aspects of behavioral response inhibition (). Fine and colleagues (2023) showed that tFUS targeted to the rIFG significantly decreased P300 response latencies and enhanced response inhibition in healthy volunteers (). Another recent study found faster reaction times to “go” signals in cognitive tasks following tFUS delivery to the right inferior frontal cortex (IFC) (). Magnetic resonance imaging revealed the enhancement of reaction time was correlated with a decrease in functional connectivity between the IFC and post-central gyrus (). There were also significant changes in the functional connectivity between IFC and the anterior cingulate cortex superior frontal cortex that evolved over 20–40 min following brief tFUS treatment (). These observations collectively demonstrate that tFUS can be used to enhance various aspects of cognitive control networks in healthy humans. Until recently however, it has been difficult to explore how these approaches may be used in human-machine interfaces due to the power requirements and size of equipment needed to conduct tFUS and ultrasonic neuromodulation. Several engineering breakthroughs led to the development of miniaturized transducers and systems that could be used in various applications (Tyler et al., 2018). Remarkably, it has recently been demonstrated that wearable tFUS devices with integrated EEG sensors that are comfortable enough to wear during sleep (Meads et al., 2024) are useful for modulating deep-brain thalamic targets in humans (Figure 5) (). Ongoing human factors, science, and engineering efforts should be aimed at advancing tFUS or TUS to enhance HMI’s and BCI’s in robotic and teleoperation.
FIGURE 5
Brain-computer interfaces for augmenting teleoperation of robotic and semi-autonomous machines
Over the past several decades, the study, development, and testing of BCI’s spans many disciplines, philosophies, and approaches. Therefore, there has already been significant progress in advancing both noninvasive and invasive BCI hardware and software for various applications ranging from prosthetic limb control to providing sensory inputs to the brain to telerobotic operation to enhancing cognition (Hotson et al., 2016; Klaes et al., 2014; Golub et al., 2016; Tonin et al., 2020) (; ; Kryger et al., 2017). Here we restrict our discussion to noninvasive BCI approaches that can be readily scaled across healthy populations of teleoperators and remote pilots or machine operators. Integrating electroencephalographic EEG or fNIRS sensors into existing training and operations procedures can be readily accomplished to monitor cognitive load, stress, or attention. These approaches have been used, for example, to monitor the psychophysiological and cognitive states of drone pilots (Khan and Hong, 2017; ; ). Some proposed taVNS-EEG closed-loop systems for modulating sleep, cognition, and attention can be designed to fit in the external ear resulting in a usability with a high degree of comfort posing minimal distractions to human operators (Tyler, 2025; Ruhnau and Zaehle, 2021). As discussed below, such designs and approaches will be advantageous to applications intended to enhance human teleoperator performance.
Several BCI studies have shown that brain signals recorded from EEG and fNIRS sensors can be used to directly control drone flight and behavior (Kim et al., 2014; ; Khan and Hong, 2017; ; Kim S. et al., 2021; Lee et al., 2021; Zafar et al., 2018). While these types of approaches are enticing to explore, we are limiting our discussion to potential BCI embodiments in which brain activity, cognitive networks, and behaviors of the human operators are modulated based on sensor information reporting engagement, work effort, attention, arousal/stress, and other environmental factors. We note this contrasts with BCIs where brain or physiological activity sensors and measures are used for machine control. Several physiological markers including EEG, eye movements, pupil dilation, and sudomotor activity have been to be reliable measures of attention and vigilance (Figure 6). While heart rate and respiration rate can reflect arousal and stress, EEG has also proven useful for monitoring other aspects of cognition including mental workload and fatigue, engagement, and abstraction (; ; Souza and Naves, 2021; ; ; Kumar and Kumar, 2016).
FIGURE 6
Enhancing sustained attention or vigilance and reducing mental fatigue are feasible, early targets for demonstrating closed-loop, noninvasive neuromodulation BCIs intended to teleoperator performance and human-machine cognition. For example, triggering tFUS delivered to the rIFG in response to changes over time in the amplitudes or latencies of P300 potentials may be a relatively simple way to enhance teleoperator cognitive control during long periods (hours) of sustained operation. Other EEG signals, such as theta (4–8 Hz) and alpha (8–12 Hz) power from central-parietal brain regions during the pre-stimulus period, are reliable markers of psychomotor vigilance that have been used to predict response time (). Frontal theta power has been shown to be a reliable index of cognitive workload (). A major limitation in the use of noninvasive BCI’s has been related to low ecological validity (lab-based vs. real-world studies) and low signal reliability. To address this several approaches are being pursued. For example, a recent study examining cognitive states in real-world environments showed increased frontal theta and decreased central delta (0–4 Hz) EEG brain wave amplitudes during periods of increased attention with the converse observed during distraction (Kaushik et al., 2022). The reliability of natural EEG brain signals can be greatly enhanced by inducing steady state evoked potentials (SSEPs). SSEPs are created by flickering stimuli on-and-off at a set frequency and can be generated with visual (; Gulbinaite et al., 2019), auditory, and somatosensory stimuli (Muller-Putz et al., 2006). The increase in spectral power induced at the flicker frequency varies with attention and are often used in machine controlling BCI applications (; Muller-Putz et al., 2006; Su et al., 2020; Giabbiconi et al., 2004). The spectral, power, and spatial characteristics of EEG and other biomarkers can be monitored to trigger tVNS and/or tFUS stimulation in a closed-loop system manner for augmenting human operator cognition and performance (Figure 6). Emerging data fusion efforts using multimodal sensing approaches combined with conventional machine learning methods will improve the reliability and scalability of closed-loop systems used for enhancing teleoperator cognition.
Conclusion
Several neurotechnologies hold promise for improving human-machine cognition and performance during the training and operation of drones, robotic systems, and semi-autonomous vehicles. Evidence has shown that noninvasive neuromodulation methods like tES, tVNS, and tFUS can improve cognitive functions such as attention, learning, and memory, as well as sensorimotor abilities. These enhancements can produce unique benefits for remote pilots and robotic teleoperators, who face complex cognitive tasks in demanding operational environments. By incorporating these noninvasive neuromodulation approaches into training programs, we can create more resilient, focused, and efficient remote pilots and teleoperators, ultimately improving the safety and efficacy human-machine operations.
Looking forward, the applications of tES, tVNS, and tFUS as part of a broader neurotechnology strategy has the potential to transform HMI’s. Future research should continue to refine noninvasive neuromodulation targets and protocols while expanding its integration with other neurotechnologies, such as EEG BCIs, to develop closed-loop systems that provide real-time cognitive enhancement during teleoperation of robotic and semi-autonomous machines and drones. Additionally, as these technologies evolve, it will be essential to validate their long-term benefits in real-world scenarios, ensuring that they contribute not only to individual human operator performance but also to broader industrial safety and workforce readiness. The approaches discussed may be useful for solutions intended to improve HCIs in the larger robotics industry. For example, it should be explored whether open- and closed-loop neuromodulation approaches can be used to enhance other human-robot interactions, where we cooperate with machines using specific skills and knowledge to accomplish large or complex tasks in manufacturing, construction, medicine, shipping, transportation and delivery, city and vehicle maintenance, deep-sea and space exploration, and other areas. Through continued innovation and convergence research, different neurotechnologies and approaches discussed can help augment training and operational excellence in human-machine interactions.
Statements
Author contributions
WT: Writing – original draft, Writing – review and editing. AA: Writing – review and editing. CLB: Writing – review and editing. AM: Writing – review and editing. CB: Writing – review and editing. MS: Writing – review and editing. AU: Writing – review and editing.
Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This material is based upon work supported by the National Science Foundation under Grant No. NCS-FO-2220677 to WT, MS, and CB. The development of BRAIN Buds was funded based on research sponsored by Air Force Research Laboratory under agreement number FA8650-18-2-5402. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation thereon.
Conflict of interest
WT is a co-founder of IST, LLC and inventor of patents covering neuromodulation methods and devices, which are described in this manuscript.
The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
Author disclaimer
The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory (AFRL) or the U.S. Government.
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Summary
Keywords
human-machine, neurotechnology, neuromodulation, BCI, cognition, teleoperations, attention
Citation
Tyler WJ, Adavikottu A, Blanco CL, Mysore A, Blais C, Santello M and Unnikrishnan A (2025) Neurotechnology for enhancing human operation of robotic and semi-autonomous systems. Front. Robot. AI 12:1491494. doi: 10.3389/frobt.2025.1491494
Received
05 September 2024
Accepted
24 April 2025
Published
23 May 2025
Volume
12 - 2025
Edited by
Jean Faber, Universidade Federal de São Paulo, Brazil
Reviewed by
Claire Marie Rangon, Independent Researcher, France
Daniel Leal Pinheiro, Scuola Superiore Sant’Anna, Italy
Updates
Copyright
© 2025 Tyler, Adavikottu, Blanco, Mysore, Blais, Santello and Unnikrishnan.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: William J. Tyler, wjpt@uab.edu
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.